Traditional target detection and recognition techniques use a single statistical, template matching, or model-based algorithm. Statistical approaches such as neural networks, decision trees, and fuzzy logic have the advantage of low data throughput requirements but suffer from the fact that one statistical algorithm is not capable of learning about a large target set under all sensing conditions. Template matching approaches outperform statistical approaches but require a large throughput to match the sensed target signature to all pre-stored target templates at all aspect angles. Model-based approaches outperform template matching approaches but require excessive throughput due to the need for real-time rendering of all pre-stored target models for all potential orientations and matching with the sensed signature.
There is a need for a target detection and recognition system that overcomes the limitations of prior systems.